A major deficiency of simulations created for, but not limited to, entertainment such as video games and training applications such as military, aviation, medical and financial simulations, is the inability of simulation agents to intuitively, instinctively and logically predict future simulation states based upon unanticipated data inputs into the simulation; and respond with proactive aggressive actions which evolve the simulation in a manner that is more challenging and realistic for human participants. Prior art discloses systems and methods for simulation agents that are based primarily upon logic-driven models and algorithms, all of which break down when the simulation data falls outside of expected parameters and logical rules.
Essentially all current artificial intelligence methods focus on logical decision making and learning approaches based upon logical causes and effects related to past experiences and known scenarios. A number of methods assume the human mind behaves in a “logical” (e.g., Bayesian statistics) manner and makes decisions based upon probabilistic outcomes as described in U.S. Pat. No. 8,831,754, “Event Prediction Using Hierarchical Event Features”, incorporated herein by reference in its entirety. There have been many other attempts to incorporate logical decision-making and learned behavior into simulation games created for, but not limited to, entertainment and training applications, through the use of pre-programmed databases and logical rules accessed by simulation agents; but these approaches result in limited behavioral responses and patterns by the simulation agents; and do not accurately emulate the organically instinctive, forward looking, predictive, emotional, and aggressively proactive behavior patterns that enable humans to make decisions based upon intuition and instinct combined with logical reasoning. For example, in video games the agents' actions are eventually anticipated by their human opponents thereby rendering the video game much less challenging to human opponents. Traditional video gaming agents are restricted to actions driven by pre-programmed logical rules which select from a pre-defined database of actions randomly or statistically, thereby making it impossible for the gaming agents to anticipate and adapt to unexpected changes in the human players' behavior during the game. More advanced “learning” approaches that implement an adaptive element to gaming agents have focused on pre-programmed; rule based reward-driven techniques as described in U.S. Pat. No. 7,837,543 B2, “Reward-Driven Adaptive Agents for Video Games”; and dynamic and genetic scripting as described by “Adaptive Artificially Intelligent Agents in Video Games: A Survey” both incorporated herein by reference in their entirety. A similar approach is described in U.S. Pat. No. 7,025,675, “Video Game Characters Having Evolving Traits”, incorporated herein by reference in its entirety, which uses a pre-programmed decision algorithm to assign “performance traits” such as speed, strength, lifetime, etc. to agents, as well as how these performance traits are utilized (e.g., aggressively or passively”) based upon real time experiences of the simulation agents (e.g., injuries, achieved goals etc.). All of these approaches are based upon logical reasoning rules such as deductive reasoning, abductive reasoning, cause-based reasoning, inductive reasoning, metaphorical mapping and fuzzy logic (e.g. “Processing Device with Intuitive Learning Capability”; “Reasoning Engines”; Systems and Methods for Artificial Intelligence Decision Making in a Virtual Environment”, which are incorporated herein by reference in their entirety). There have been additional disclosures related to simulating human consciousness such as described in U.S. Pat. No. 8,346,699, “System and Method for Simulating Consciousness”, incorporated herein by reference in its entirety; but even these disclosures are based upon pre-programmed data sets with metrics defining “feelings”, “actions” and “goals” which are correlated during the simulation to artificially replicate a form of limited consciousness which is limited by the spectrum of the data pre-programmed into the simulation.
None of these approaches enable the simulation with an organically instinctive, forward looking, predictive, emotional, and aggressively proactive behavior patterns that emulate the organically intuitive, instinctive and logical capabilities of humans, with the ability to anticipate, forecast, predict, and aggressively react to possible future game states that fall outside the spectrum of possibilities pre-programmed into the game; and proactively initiate aggressive actions that pre-empt these non-programmed future game states that would be detrimental to the simulation agents; and favor states that are more attractive to the simulation agents, thereby resulting in a simulation that is more challenging and realistic to human participants.
Similar issues exist with typical training simulations for military, aviation, medical and financial applications, to name a few, with scenarios that have been pre-programmed into the simulations thereby limiting the effectiveness of the training simulation to represent reality. One example is disclosed in U.S. Pat. No. 8,346,646, “Financial Market Replicator and Simulator”, incorporated herein by reference in its entirety, which uses past financial market data pre-programmed into the simulation allowing users of the simulation to input simulated trades to test a trading strategy. This form of training does not reflect how the market changes as other external trades take place, whereas in real-life the financial market is a fully duplexed system which is constantly changing as internal and external trades influence one another. Therefore these kinds of “one way” training simulations do not effectively challenge and train the user to develop trading strategies where the real-time financial market data is rapidly changing do to other external factors, resulting in market data that is very different from that which was pre-programmed into the simulation. A financial organically instinct-driven simulation agent enabled with organically instinctive, forward looking, predictive, emotional and aggressively proactive capabilities would evolve the financial simulation in a manner that more accurately reflects what happens in an actual trading market, thereby offering a much more realistic and challenging training aid for human financial investors.
Therefore, there is a need for simulations to enable instinct-driven simulation agents with behavior patterns that are organically instinctive, forward looking, predictive, emotional and aggressively proactive through the use of mathematical methodologies and techniques that organically emulate the ability of humans to make decisions based upon intuition and instinct combined with logical reasoning, thereby enabling organically instinct-driven simulation agents to evolve the simulation in a more challenging and realistic manner for human participants; such as proactively initiating aggressive actions that pre-empt simulation states that are detrimental to the organically instinct-driven simulation agents in favor of simulation states that are more attractive to the organically instinct-driven simulation agents for entertainment applications; and initiating aggressive actions in training simulations that realistically evolves the simulation in a manner that is more challenging and realistic for human participants.